Neural Network Model for Greenhouse Microclimate Predictions

Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential...

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Bibliographic Details
Published in:Agriculture (Basel) 2022-06, Vol.12 (6), p.780
Main Authors: Petrakis, Theodoros, Kavga, Angeliki, Thomopoulos, Vasileios, Argiriou, Athanassios A.
Format: Article
Language:English
Subjects:
Actuators
Agriculture
Algorithms
Artificial intelligence
Back propagation
Back propagation networks
Computer applications
decision support system
Decision support systems
Energy consumption
Food consumption
Food production
greenhouse
Greenhouses
Humidity
Internet of Things
Irradiance
Levenberg–Marquardt
Microclimate
Microorganisms
multilayer perceptron
Multilayer perceptrons
Neural networks
neural networks model
Relative humidity
temperature modeling
Temperature perception
Wind speed
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Summary:Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential aspects, the appropriate greenhouse environment should be maintained using a computational decision support system (DSS), which will be especially adaptable to changes in the characteristics of the external environment. A multilayer perceptron neural network (MLP-NN) was designed to model the internal temperature and relative humidity of an agricultural greenhouse. The specific NN uses Levenberg–Marquardt backpropagation as a training algorithm; the input variables are the external temperature and relative humidity, wind speed, and solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modeled timestep. The maximum errors of the modeled temperature and relative humidity are 0.877 K and 2.838%, respectively, whereas the coefficients of determination are 0.999 for both parameters. A model with a low maximum error in predictions will enable a DSS to provide the appropriate commands to the greenhouse actuators to maintain the internal conditions at the desired levels for cultivation with the minimum possible energy consumption.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture12060780